{"id":"W3043242728","doi":"10.1007/s10895-020-02586-z","title":"Detecting Mercury (II) and Thiocyanate Using “Turn-on” Fluorescence of Graphene Quantum Dots","year":2020,"lang":"en","type":"article","venue":"Journal of Fluorescence","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Zabol University of Medical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Thiocyanate; Quantum dot; Fluorescence; Mercury (programming language); Graphene; Photochemistry; Quenching (fluorescence); Citric acid; Inorganic chemistry; Nanotechnology; Organic chemistry; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001833692,0.0003425985,0.0001614103,0.0002597294,0.000222223,0.0003874228,0.0006425538,0.0007816815,0.0007427029],"category_scores_gemma":[0.0002566088,0.0002316021,0.0002749358,0.0001747733,0.0004841868,0.0002698443,0.0003058705,0.000454364,0.0001582626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004431572,"about_ca_system_score_gemma":0.0001804826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264453,"about_ca_topic_score_gemma":0.002009732,"domain_scores_codex":[0.9997479,0.0000554217,0.000007373572,0.00006474277,0.00007496867,0.00004948907],"domain_scores_gemma":[0.9998838,0.00003894811,0.00001752357,0.00001602992,0.00002463307,0.00001898504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007679132,0.00001170626,0.00024794,0.00002299838,0.000008712984,0.00007095591,0.00002871206,0.0001241883,0.9977173,0.0002406376,0.00006256732,0.001387584],"study_design_scores_gemma":[0.000004114025,0.00004497567,0.0005603867,0.000001894155,0.000006506315,0.00005189669,0.00001652115,0.002087912,0.9968419,0.00004978515,0.0003285914,0.000005494736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898976,0.0003027047,0.007170421,0.0001563184,0.00004856471,0.00001597612,0.0000959242,0.0001446852,0.002167716],"genre_scores_gemma":[0.9938551,0.0001478147,0.004896938,0.00006152733,0.000008505574,0.000008917634,0.00007051237,0.000009769449,0.0009408274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001264453,"threshold_uncertainty_score":0.003215373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03487531186411139,"score_gpt":0.2722719510762562,"score_spread":0.2373966392121448,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}